Spaces:
Running
Running
| import streamlit as st | |
| import pandas as pd | |
| from utils import extract_from_url, get_model, calculate_memory | |
| import plotly.express as px | |
| import numpy as np | |
| st.set_page_config(page_title='Can you run it? LLM GPU check', layout="wide", initial_sidebar_state="expanded") | |
| st.title("Can you run it? LLM GPU check") | |
| percentage_width_main = 80 | |
| st.markdown( | |
| f"""<style> | |
| .appview-container .main .block-container{{ | |
| max-width: {percentage_width_main}%;}} | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| def get_gpu_specs(): | |
| return pd.read_csv("data/gpu_specs.csv") | |
| def get_name(index): | |
| row = gpu_specs.iloc[index] | |
| return f"{row['Product Name']} ({row['RAM (GB)']} GB, {row['Year']})" | |
| def create_plot(memory_table, y, title, container): | |
| fig = px.bar(memory_table, x=memory_table.index, y=y, color_continuous_scale="RdBu_r") | |
| fig.update_layout(yaxis_title="Number of GPUs", title=dict(text=title, font=dict(size=25))) | |
| fig.update_coloraxes(showscale=False) | |
| container.plotly_chart(fig, use_container_width=True) | |
| gpu_specs = get_gpu_specs() | |
| access_token = st.sidebar.text_input("Access token") | |
| model_name = st.sidebar.text_input("Model name", value="mistralai/Mistral-7B-v0.1") | |
| if not model_name: | |
| st.info("Please enter a model name") | |
| st.stop() | |
| model_name = extract_from_url(model_name) | |
| if model_name not in st.session_state: | |
| model = get_model(model_name, library="transformers", access_token=access_token) | |
| st.session_state[model_name] = (model, calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"])) | |
| gpu_vendor = st.sidebar.selectbox("GPU Vendor", ["NVIDIA", "AMD", "Intel"]) | |
| # year = st.sidebar.selectbox("Filter by Release Year", list(range(2014, 2024))[::-1], index=None) | |
| gpu_info = gpu_specs[gpu_specs['Vendor'] == gpu_vendor].sort_values('RAM (GB)', ascending=False) | |
| # if year: | |
| # gpu_info = gpu_info[gpu_info['Year'] == year] | |
| min_ram = gpu_info['RAM (GB)'].min() | |
| max_ram = gpu_info['RAM (GB)'].max() | |
| ram = st.sidebar.slider("Filter by RAM (GB)", min_ram, max_ram, (min_ram, max_ram), step=0.5) | |
| gpu_info = gpu_info[gpu_info["RAM (GB)"].between(*ram)] | |
| gpu = st.sidebar.selectbox("GPU", gpu_info['Product Name'].index.tolist(), index=21, format_func=lambda x : gpu_specs.iloc[x]['Product Name']) | |
| gpu_spec = gpu_specs.iloc[gpu] | |
| gpu_spec.name = 'INFO' | |
| lora_pct = st.sidebar.slider("LoRa % trainable parameters", 0.1, 100.0, 2.0, step=0.1) | |
| st.sidebar.dataframe(gpu_spec.T) | |
| memory_table = pd.DataFrame(st.session_state[model_name][1]).set_index('dtype') | |
| memory_table['LoRA Fine-Tunning (GB)'] = (memory_table["Total Size (GB)"] + | |
| (memory_table["Parameters (Billion)"]* lora_pct/100 * (16/8)*4)) * 1.2 | |
| _, col, _ = st.columns([1,3,1]) | |
| with col.expander("Information", expanded=True): | |
| st.markdown("""- GPU information comes from [TechPowerUp GPU Specs](https://www.techpowerup.com/gpu-specs/) | |
| - Mainly based on [Model Memory Calculator by hf-accelerate](https://huggingface.co/spaces/hf-accelerate/model-memory-usage) | |
| using `transformers` library | |
| - Inference is calculated following [EleutherAI Transformer Math 101](https://blog.eleuther.ai/transformer-math/), | |
| where is estimated as """) | |
| st.latex(r"""\text{Memory}_\text{Inference} \approx \text{Model Size} \times 1.2""") | |
| st.markdown("""- For LoRa Fine-tunning, I'm asuming a **16-bit** dtype of trainable parameters. The formula (in terms of GB) is""") | |
| st.latex(r"\text{Memory}_\text{LoRa} \approx \text{Model Size} + \left(\text{ \# trainable Params}_\text{Billions}\times\frac{16}{8} \times 4\right) \times 1.2") | |
| st.markdown("- You can understand `int4` as models in `GPTQ-4bit`, `AWQ-4bit` or `Q4_0 GGUF/GGML` formats") | |
| _memory_table = memory_table.copy() | |
| memory_table = memory_table.round(2).T | |
| _memory_table /= gpu_spec['RAM (GB)'] | |
| _memory_table = _memory_table.apply(np.ceil).astype(int).drop(columns=['Parameters (Billion)', 'Total Size (GB)']) | |
| _memory_table.columns = ['Inference', 'Full Training Adam', 'LoRa Fine-tuning'] | |
| _memory_table = _memory_table.stack().reset_index() | |
| _memory_table.columns = ['dtype', 'Variable', 'Number of GPUs'] | |
| col1, col2 = st.columns([1,1.3]) | |
| with col1: | |
| st.write(f"#### [{model_name}](https://huggingface.co/{model_name}) ({memory_table.iloc[3,0]:.1f}B)") | |
| st.write(memory_table.iloc[[0, 1, 2, 4]]) | |
| with col2: | |
| num_colors= 4 | |
| colors = [px.colors.sequential.RdBu[int(i*(len(px.colors.sequential.RdBu)-1)/(num_colors-1))] for i in range(num_colors)] | |
| fig = px.bar(_memory_table, x='Variable', y='Number of GPUs', color='dtype', barmode='group', color_discrete_sequence=colors) | |
| fig.update_layout(title=dict(text=f"Number of GPUs required for<br> {get_name(gpu)}", font=dict(size=25)) | |
| , xaxis_tickfont_size=14, yaxis_tickfont_size=16, yaxis_dtick='1') | |
| st.plotly_chart(fig, use_container_width=True) | |